Page Updated On July 6, 2022

How AI & Automation Can Change Data Abstraction Process Flow in 2022?

Data is a discrete piece of factual information that is recorded and utilized for analysis. It is the raw data from which statistics are generated to support business decision making, market analysis, customer sentiment analysis and competitor monitoring. Data is essential in today’s corporate environment. In reality, making large changes and enjoying development is practically impossible if you don’t maintain track of the different variables influencing the market on a regular basis. As businesses expand the amount of technology employed in fundamental processes, the amount of data recorded and retained for future analysis grows, which also increases irrelevant details in the database. Only a handful of businesses thrive at translating raw data into meaningful information, despite spending a lot on data collection, storage, and management.

This is especially true for unstructured material, which still accounts for around 80 to 90 percent of all business data.

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According to a recent Veritas analysis, 52% of all data now kept and processed by organizations across the world is classified as “dark,” with an unknown value. Up to 33% of data is deemed redundant, outmoded, or insignificant, and is even known to be worthless. Only 15% of all stored data is considered business important on average.

To resolve this problem, data abstraction is important. Data abstraction is the process of removing unnecessary or less important data from the database and delivering a simplified representation of the whole. Database systems are made up of complicated data structures. Developers use abstraction to conceal unimportant information from users in order to make the system effective in terms of data retrieval and minimize complexity in terms of user-friendliness. This method makes database design easier. It also depends on the end data user to what needs to be removed. For instance, an HR manager doesn’t need to know the candidate’s medical record but needs his previous work experience while hiring. How would they filter the record to find the relevant candidate? At this moment data abstraction will be helpful. At an enterprise level, data cleaning and organizing are critical to meet data-driven decisions. Data abstraction helps remove the redundant features and reduce the size of the file. The increased need for keeping data clean and structured has resulted in the introduction of several services in recent years. Data mining, data cleansing, data conversion, and data abstraction services are all aimed to make the data easy to use. Using AI technology, service providers are delivering data abstraction services and changing the process flow to positive.

Three Levels Of Data Abstraction

Data independence means that consumers and data should not interact directly with one another. The user should be on a separate level, and the data should be on a different level. Data Independence can be obtained in this manner. So, let’s take a closer look at the three layers of data abstraction.

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Physical or internal level

It is the lowest level of abstraction for database systems. The Physical level, as the name implies, shows us where the data is actually saved, i.e. the actual location of the data that the user is storing. The Database Administrators (DBA) decide which data should be maintained on which hard drive, how the data should be fragmented, where it should be saved, and so on.

Logical or conceptual level

The logical level is the next higher level or intermediate level. It describes what data is kept in the database and how that data is connected to one another. This degree of abstraction satisfies the organizational data needs. We frequently use the term “conceptualization” to refer to the overarching notion about the circumstance at hand. It is an organization-wide depiction of data as seen by top-level executives. At this level, the primary data objects are identified and described in as much detail as possible. This is where the data in terms of any relationships that may exist between them are examined. The most generally used conceptual model is employed to gain a better picture of the data presented.

View or external level

It is the most advanced level. This level instructs the programme on how the data should be shown to the user. There are various tiers of views at the view level, and each view only defines a part of the whole data. It also facilitates user engagement by providing a variety of views or multiple views of the same database.

Explanation With Example

Assume we’re storing customer data in a customer table. At the physical or internal level, these records can be characterized as memory storage blocks. These particulars are frequently concealed from programmers. At the logical or conceptual level, these records are specified as fields and attributes with data types, and their relationships with one another can be logically implemented. Programmers typically work at this level since they are familiar with database systems. At the view or external level, the user only interacts with the system via the GUI and enters the details on the screen; they are unaware of how and what data is kept; such details are concealed from them. Although data abstraction offers clean data for enterprise data needs, it lacks efficiency. AI-augmented data abstraction is whole another story, they use AI algorithms to streamline the process, reduce human interventions and automate the process making data abstraction more efficient and reliable. The ideal feature of Al is its capacity to reason and execute actions that have the highest possibility of reaching a certain objective, based on the concept that human intelligence can be stated in such precise terms that a computer can duplicate it. CTA (Image): Outsource BigData provides AI Augmented Data Abstraction services to SMB and corporate companies. CTA Button: Get a free trial of AI-augmented data abstraction services and streamline your process.

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How AI Can Change Data Abstraction Process Flow?

The increased use of artificial intelligence (AI) and machine learning (ML) in the business sector has the goal of making businesses both efficient, and secure.

Currently, 83% of businesses consider AI to be a strategic priority. According to 59% of executives, the deployment of AI would improve big data in their organization.

From refining data to making it structured, AI plays a major role when integrated with a business database. When it comes to data abstraction, AI can completely change the game. Here is how

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1. Identify The Necessary Data Entities

It involves identifying the parts or entities of data that are needed and removing the data entities that are invalid or irrelevant. Artificial Intelligence makes this process more accurate, less time-consuming, and flawless.

According to a report by Accenture, AI technology has the potential to increase corporate efficiency by up to 40%.

It is very important that this phase of data abstraction is done rightly, and AI ensures this by bringing efficiency and quality to data. AI technology is smart enough to identify and remove irrelevant data from the database. It involves a view level of data abstraction. AI technology when integrated into the data abstraction process enables a device to solve problems that require human intelligence and the efficiency of the machine. AI algorithms are capable of classifying data and making wiser decisions that change and adapt over time. They have the potential to open up new use cases while also increasing the flexibility of existing algorithms. For integrating automation in the data abstraction process, businesses are choosing data abstraction services from certified tech experts.

2. Identify Key Properties Of Entities

Next, the select data entities are broken down to identify the properties or attributes that the entity possesses. For example if the data entity is work experience, then the attributes would be years of experience, types of job roles, and previous companies’ names. AI removes the human intervention in the data filtration process and also makes it more accurate since the AI has the capability to accurately assort the properties of a data entity.

48% of companies utilize machine learning, data analysis, and artificial intelligence techniques to ensure the accuracy of their data.

The entire process of filtering data is resolved and made effortless with AI-powered data abstraction in the process. Accuracy is also a significant factor whether organizations outsource (either wholly or partially) the development of an AI system to a third party or acquire AI-supported data abstraction services from an external vendor. Accurate data abstraction supports beneficial decision-making within your business.

3. Connect The Dots – Find Relations Among The Entities

The first draft of the data abstraction should show the relations among the data entities, this involves a logical level of data abstraction. Manually connecting data and finding relations among the entities is extremely challenging and time taking Implementation of AI in this process resolves this issue and makes the process of connecting data patterns seamless and more reliable. The AI algorithm will identify the relations or patterns among the data entities with laser-focused accuracy. It is reliable since the algorithm is put through a rigorous process of such identification and in every cycle it adapts and learns, thus making the following cycle more quick and accurate. AI-powered data abstraction enables scalability and can keep up with the growing demands of your business. AI-driven data abstraction services from professionals should be your first choice to deploy the AI model in the process more seamlessly.

4. Map The Properties To The Entities

There is one more significant relational network to draw from these data entities and their properties. It helps us easily understand and visualize their inter-dependency and how each property or entity can alter the other. With AI and automation, it is done in less time. AI has the ability to accelerate data transformation for faster data onboarding. Because decisions are made based on previously gathered information using a certain set of machine learning algorithms, the risk of mistakes such as duplications, missing data, and so on is reduced. As a result, artificial intelligence not only properly maps disparate data sources to target areas but also preserves data integrity to radicalize decision-making and entirely transform the way you do business. AI speeds up and improves the accuracy of property mapping. Using machine learning to infer data mapping predictions from an existing library of tested and certified data maps, AI reduces the work and time necessary to build intelligent data mappings significantly. Furthermore, using AI in mappings streamlines data integrations, which provides value to your organization.

5. Remove Or Prevent Duplicate Data Entities

Duplicate data is the most prevalent data quality concern. Duplicate data can be any record in your database that mistakenly shares data with another record. A full carbon copy of another record is the most noticeable type of duplicate data. These are difficult to detect and frequently arise when data is transferred across systems. Missing values and duplicate data are two variables that have a negative influence on data quality. When measured in monetary terms, the cost of duplicate and dead data is frightening.

The average B2B organization’s data volume doubles every 12 to 18 months, so even if data is pretty clean today, it is usually just a matter of time until things break down. According to studies, data quality concerns destroy up to 12% of total income.

So, it is necessary to identify and remove duplicate data from the database. The gross data may consist of more than one same property, but it might be related to two different entities. If not removed it will reduce the efficiency of the data consumers. With the integration of AI in the data abstraction process, it becomes effortless to find and remove duplicate data entities from the database. AI algorithms when applied to data abstraction consistently prevent data decay and duplicates. AI is designed to grow beyond the establishment of a complicated rule-based framework. Preventing faulty data is accomplished through the use of AI augmentation. Professional AI-driven data abstraction services are mostly obtained from experts for saving time and money.

6. Validation Of The Outcome

This is the endpoint of the data abstraction process, it involves verifying or validating the data abstracted against the desired data. And, later storing it in databases, is the physical level of data abstraction. When AI is used in the data validation process, everything becomes uncomplicated and time-saving. As the volume of data grows, it is critical that data-driven businesses use proactive strategies to monitor and maintain data quality on a regular basis. Otherwise, they run the danger of acting on insights based on faulty data. Choosing professionals for data abstraction services is an ideal choice to get validated data outcomes. AI algorithms applied by the expert in the data abstraction process helps in making the database accurate, high in quality and powerful.

Conclusion

The technological problems associated with dealing with a 100x or more growth in data quantities are complicated. These can lead to significant architectural and technological concerns. Data abstraction is extremely valuable since it enables people to straightforward. comprehend and design complicated databases efficiently. When deployed Artificial Intelligence in the data abstraction, it makes everything effortless. Business professionals have more time to focus on core business activities when a smart AI-powered system handles data abstraction. For AI-driven data abstraction services, you can choose Outsource BigData, a trusted tech company. Visit the official website of Outsource BigData and learn more about their service.

AIMLEAP Automation Practice

As part of AIMLEAP Business, AIMLEAP Outsource Bigdata practice provides advanced data collection and management expertise, as well as Robotic Process Automation (RPA) capabilities that help clients create highly personalized digital experiences, products and services. Our RPA solutions help customers with insights from data for decision-making, improve operations efficiencies and reduce costs. To learn more, visit us www.outsourcebigdata.com

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